Much of the empirical and theoretical basis for visualization work comes from studies examining the efficiency of visual channels in extracting information, and using these results to generate a ranking of these channels [1]}. These rankings power many of our design guidelines and constraints [2]}, are ubiquitous in our textbooks [3]}, [4]}, and are instantiated in the logic of many of our automated or semi-automated visualization design tools [5]}, [6]}. And yet, these rankings do not seem to capture important components of how people use, interpret, and learn from visualizations. We should be expansive in how we analyze, conceptualize, and teach visualization. Otherwise, we risk a situation where academia focuses on the narrow, scatterplot-like section of the vast, more interesting world of visualization as a whole.
